1. Field of the Invention
The present invention relates to an image processing device and image processing method capable of converting for example a low-quality image into a high-quality image by interpolation.
2. Description of the Related Art
For example when converting a low-quality digital image to a high-quality digital image, converting a low resolution image to high resolution, or enlarging an image, image interpolation is performed by for example inserting new pixels between one original pixel and another. Known methods of digital image interpolation include for example the nearest-neighbor interpolation method (also called the zero-order holding method), linear interpolation method (also called the straight-line interpolation method, collinear interpolation method, or bilinear method), cubic convolution interpolation method (also called the bi-cubic method).
Since the basic concept of the methods of interpolation described above is interpolation using a sinc function based on the theory of sampling, they are theoretically correct only when the original image consists of frequency components of no more than half the Nyquist frequency. However, since the frequency components contained in an actual original image are infinitely large, it is not possible to restore high frequency components contained in the original image by the aforesaid interpolation methods.
The frequency conversion method has therefore been proposed as a technique for interpolating high frequency components lost in such sampling processes. A well-known example of the frequency conversion method is the Gerchberg-Papoulis iteration method (GP method), in which an operation wherein band-limited frequency components in a frequency range are projected in real space and only a restricted range of the total real space components is projected in frequency space, a band-limited portion of the total frequency components being again projected in real space after substitution with the original frequency components, which are known, is repeated infinitely many times. Usually, the computational load is reduced by employing the DCT algorithm for frequency conversion (IM-GPDCT method).
However, processing time is lengthened by the fact that it is necessary to repeat the DCT algorithm or inverse DCT algorithm until suitable high-frequency components are obtained. Also, there is a risk that picture quality will be lowered by emphasizing noise or generation of ringing.